{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"MnistNet.ipynb","provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"qiTfW8RdI72q","colab_type":"code","colab":{}},"source":["import torch\n","import random\n","import numpy as np\n","\n","random.seed(0)\n","np.random.seed(0)\n","torch.manual_seed(0)\n","torch.cuda.manual_seed(0)\n","torch.backends.cudnn.deterministic = True"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"18XtbBDkJbie","colab_type":"code","colab":{}},"source":["import torchvision.datasets"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Dciavh3jJjm0","colab_type":"code","colab":{}},"source":["MNIST_train = torchvision.datasets.MNIST('/', download=True, train=True)\n","MNIST_test = torchvision.datasets.MNIST('/', download=True, train=False)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"uXAfUe0uKNzD","colab_type":"code","outputId":"03e2ee15-ca14-49b8-f09e-9affb5af858e","executionInfo":{"status":"ok","timestamp":1569499136838,"user_tz":-180,"elapsed":968,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":153}},"source":["X_train = MNIST_train.train_data\n","y_train = MNIST_train.train_labels\n","X_test = MNIST_test.test_data\n","y_test = MNIST_test.test_labels"],"execution_count":0,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:53: UserWarning: train_data has been renamed data\n","  warnings.warn(\"train_data has been renamed data\")\n","/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:43: UserWarning: train_labels has been renamed targets\n","  warnings.warn(\"train_labels has been renamed targets\")\n","/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:58: UserWarning: test_data has been renamed data\n","  warnings.warn(\"test_data has been renamed data\")\n","/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:48: UserWarning: test_labels has been renamed targets\n","  warnings.warn(\"test_labels has been renamed targets\")\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"HRjRcoTwK4Sx","colab_type":"code","outputId":"1e4057e0-f680-44da-c746-f4d68bb0ad06","executionInfo":{"status":"ok","timestamp":1569499136838,"user_tz":-180,"elapsed":836,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["X_train.dtype, y_train.dtype"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(torch.uint8, torch.int64)"]},"metadata":{"tags":[]},"execution_count":64}]},{"cell_type":"code","metadata":{"id":"SKx1lfP0K-2x","colab_type":"code","colab":{}},"source":["X_train = X_train.float()\n","X_test = X_test.float()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"XeJ5KyezQRy_","colab_type":"code","outputId":"db64ba0e-b022-471e-de14-ec9350b19c10","executionInfo":{"status":"ok","timestamp":1569499137298,"user_tz":-180,"elapsed":999,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["X_train.shape, X_test.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(torch.Size([60000, 28, 28]), torch.Size([10000, 28, 28]))"]},"metadata":{"tags":[]},"execution_count":66}]},{"cell_type":"code","metadata":{"id":"LgBic1L8QWRG","colab_type":"code","outputId":"221a04a9-c315-4514-ba61-be355c9f934c","executionInfo":{"status":"ok","timestamp":1569499137768,"user_tz":-180,"elapsed":1324,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["import matplotlib.pyplot as plt\n","plt.imshow(X_train[0, :, :])\n","plt.show()\n","print(y_train[0])"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["tensor(5)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"bK5EFB3eQjju","colab_type":"code","colab":{}},"source":["X_train = X_train.reshape([-1, 28*28])\n","X_test = X_test.reshape([-1, 28*28])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Db4ZC7a8RI_l","colab_type":"code","colab":{}},"source":["class MNISTNet(torch.nn.Module):\n","  def __init__(self, n_hidden_neurons):\n","    super().__init__()\n","    self.fc1 = torch.nn.Linear(28*28, n_hidden_neurons)\n","    self.act1 = torch.nn.LeakyReLU()\n","    self.fc2 = torch.nn.Linear(n_hidden_neurons, n_hidden_neurons*5)\n","    self.act2 = torch.nn.Tanh()\n","    self.fc3 = torch.nn.Linear(n_hidden_neurons*5, n_hidden_neurons)\n","    self.act3 = torch.nn.Sigmoid()\n","    self.fc4 = torch.nn.Linear(n_hidden_neurons, 10)\n","    \n","  def forward(self, x):\n","    x = self.fc1(x)\n","    x = self.act1(x)\n","    x = self.fc2(x)\n","    x = self.act2(x)\n","    x = self.fc3(x)\n","    x = self.act3(x)\n","    x = self.fc4(x)\n","    return x\n","\n","mnist_net = MNISTNet(50)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"6wi3q0AlSvBi","colab_type":"code","outputId":"0bb9f7ce-618a-4b0a-b8bb-c4cf92aa4aa9","executionInfo":{"status":"ok","timestamp":1569499137773,"user_tz":-180,"elapsed":833,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["torch.cuda.is_available()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{"tags":[]},"execution_count":70}]},{"cell_type":"code","metadata":{"id":"BnhJlpsaT4TI","colab_type":"code","outputId":"93e8e216-1b35-4187-dfdb-f4a05bd07b42","executionInfo":{"status":"ok","timestamp":1569499140530,"user_tz":-180,"elapsed":3429,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":289}},"source":["!nvidia-smi"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Thu Sep 26 11:58:58 2019       \n","+-----------------------------------------------------------------------------+\n","| NVIDIA-SMI 430.40       Driver Version: 418.67       CUDA Version: 10.1     |\n","|-------------------------------+----------------------+----------------------+\n","| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n","| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n","|===============================+======================+======================|\n","|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n","| N/A   45C    P0    61W / 149W |  11433MiB / 11441MiB |      0%      Default |\n","+-------------------------------+----------------------+----------------------+\n","                                                                               \n","+-----------------------------------------------------------------------------+\n","| Processes:                                                       GPU Memory |\n","|  GPU       PID   Type   Process name                             Usage      |\n","|=============================================================================|\n","+-----------------------------------------------------------------------------+\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"GDy_jpGqT7Lf","colab_type":"code","outputId":"943e7b78-2ac5-4cd1-9337-85990895ca87","executionInfo":{"status":"ok","timestamp":1569499140532,"user_tz":-180,"elapsed":3257,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n","mnist_net = mnist_net.to(device)\n","list(mnist_net.parameters())"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[Parameter containing:\n"," tensor([[-0.0003,  0.0192, -0.0294,  ...,  0.0219,  0.0037,  0.0021],\n","         [-0.0198, -0.0150, -0.0104,  ..., -0.0203, -0.0060, -0.0299],\n","         [-0.0201,  0.0149, -0.0333,  ..., -0.0203,  0.0012,  0.0080],\n","         ...,\n","         [ 0.0002, -0.0095, -0.0115,  ...,  0.0018,  0.0187, -0.0318],\n","         [ 0.0034,  0.0025,  0.0278,  ...,  0.0282,  0.0256,  0.0175],\n","         [ 0.0033, -0.0187,  0.0128,  ..., -0.0224,  0.0089,  0.0118]],\n","        device='cuda:0', requires_grad=True), Parameter containing:\n"," tensor([-3.1980e-02, -1.9162e-02, -3.3994e-02, -1.0583e-02,  2.5050e-02,\n","          1.0758e-02,  5.8520e-03, -3.1078e-02,  1.3870e-02,  3.2997e-02,\n","          7.2808e-03,  2.1296e-02,  1.9349e-02, -1.0779e-02, -2.4915e-02,\n","          2.5673e-02, -1.7654e-02, -2.2887e-02, -2.7681e-02, -1.1384e-02,\n","         -1.7487e-02,  1.1514e-02, -1.2511e-02,  3.5514e-02, -1.0688e-05,\n","          2.5205e-02, -6.4219e-03,  2.8707e-02, -4.7948e-03, -1.5754e-02,\n","         -2.1942e-02,  1.7107e-02,  1.6443e-02,  3.2121e-02, -2.0468e-02,\n","         -1.4736e-02, -2.5035e-02,  2.5062e-02,  1.2945e-02, -3.1747e-02,\n","          1.4838e-02, -8.1118e-03,  7.6437e-03,  1.2465e-02, -3.1545e-02,\n","          2.8544e-02, -1.8928e-02, -3.2219e-02, -3.5375e-03, -1.1264e-02],\n","        device='cuda:0', requires_grad=True), Parameter containing:\n"," tensor([[-0.0097, -0.0915,  0.0143,  ..., -0.0776, -0.0011, -0.0474],\n","         [-0.1103,  0.0839, -0.1305,  ..., -0.0655, -0.0700, -0.0770],\n","         [ 0.0456, -0.1001, -0.1409,  ...,  0.1307, -0.0083,  0.1220],\n","         ...,\n","         [-0.1298,  0.0170,  0.0538,  ...,  0.1207, -0.0197, -0.1085],\n","         [ 0.1214, -0.0074,  0.0622,  ..., -0.0492, -0.0908, -0.0168],\n","         [ 0.0582, -0.0971,  0.1095,  ..., -0.0847,  0.0418, -0.0901]],\n","        device='cuda:0', requires_grad=True), Parameter containing:\n"," tensor([-0.0103, -0.0844,  0.0331, -0.0097,  0.1009, -0.1405,  0.1410, -0.0769,\n","          0.0596,  0.0845, -0.0109,  0.0593,  0.0264,  0.0107, -0.1202,  0.1124,\n","         -0.1262, -0.1062, -0.1123, -0.0532,  0.0089, -0.0665,  0.0342,  0.0266,\n","          0.0364, -0.0693, -0.1122, -0.0864,  0.0630,  0.1162, -0.0410, -0.0085,\n","         -0.0176,  0.0402,  0.1067,  0.1404,  0.0071,  0.0885, -0.0744, -0.0367,\n","         -0.0477, -0.0843,  0.0242, -0.0554, -0.0115, -0.0655,  0.0756, -0.1217,\n","          0.1349, -0.0981,  0.0377, -0.0651,  0.1317,  0.0901, -0.0226,  0.1090,\n","         -0.1199,  0.0877, -0.0576, -0.0463,  0.0891,  0.1124,  0.0113, -0.0303,\n","         -0.0820,  0.1011,  0.0899,  0.0608, -0.0249, -0.1223,  0.1017,  0.0171,\n","         -0.1351, -0.0286, -0.0782, -0.0112, -0.0177,  0.0718, -0.1392, -0.1146,\n","         -0.0267, -0.0271, -0.0716, -0.0389,  0.1074, -0.0507,  0.0856, -0.0455,\n","          0.0754,  0.1034,  0.0091,  0.0674, -0.0018,  0.1109,  0.0702, -0.0284,\n","          0.0132,  0.0404,  0.0176, -0.0945,  0.0256,  0.0645, -0.1146, -0.1288,\n","         -0.0712,  0.1373,  0.0697, -0.0832, -0.0329,  0.0213, -0.0604, -0.0832,\n","          0.0109,  0.0071, -0.0993, -0.0291, -0.0290,  0.0534, -0.0624,  0.0277,\n","          0.1014, -0.0983, -0.0213,  0.0707, -0.0177, -0.1208, -0.0529,  0.0957,\n","         -0.1021, -0.1390,  0.0398,  0.0115,  0.1124,  0.0516,  0.1142,  0.0227,\n","         -0.1011,  0.0210, -0.0400, -0.1080, -0.0660,  0.1162,  0.0503,  0.1342,\n","          0.0408, -0.0086,  0.0883,  0.0665, -0.0967,  0.1266,  0.0382,  0.0447,\n","         -0.0769, -0.0608, -0.1252, -0.0671,  0.1084,  0.0833, -0.1250, -0.0700,\n","         -0.0648,  0.0553,  0.0803,  0.0217, -0.0202, -0.0211, -0.0791, -0.0005,\n","         -0.1384, -0.0603,  0.1004,  0.0520, -0.0520,  0.1091, -0.1299, -0.1321,\n","          0.1096,  0.0823,  0.0299, -0.0785, -0.0884, -0.0189, -0.0533,  0.0411,\n","         -0.0107,  0.0332, -0.0004, -0.0671,  0.0617,  0.0912, -0.0176,  0.1313,\n","         -0.0160,  0.0355, -0.0725, -0.0650, -0.0906, -0.0811, -0.0742, -0.1040,\n","         -0.0395,  0.1015, -0.0948, -0.0425, -0.0404, -0.0457,  0.0595,  0.0541,\n","         -0.0967, -0.0324, -0.1399,  0.1005, -0.0753,  0.1321, -0.1339,  0.1220,\n","         -0.0601,  0.0423,  0.0939,  0.0324,  0.1154, -0.0872, -0.0293, -0.1048,\n","          0.0017,  0.0699,  0.1071,  0.1403,  0.0996, -0.0797,  0.0170, -0.1179,\n","         -0.1206, -0.1082, -0.0449,  0.1032, -0.0490, -0.0601, -0.0556, -0.0732,\n","         -0.0502, -0.0084, -0.0448,  0.0895, -0.1107, -0.1259, -0.1114,  0.1397,\n","         -0.0024,  0.0121], device='cuda:0', requires_grad=True), Parameter containing:\n"," tensor([[ 0.0426, -0.0608, -0.0240,  ..., -0.0252,  0.0202,  0.0121],\n","         [-0.0134, -0.0343, -0.0366,  ..., -0.0269, -0.0248,  0.0426],\n","         [-0.0121, -0.0347,  0.0319,  ..., -0.0541,  0.0323,  0.0510],\n","         ...,\n","         [-0.0528,  0.0549,  0.0279,  ..., -0.0250, -0.0468, -0.0213],\n","         [ 0.0307, -0.0047,  0.0333,  ..., -0.0458, -0.0222, -0.0123],\n","         [-0.0541, -0.0294, -0.0322,  ...,  0.0156,  0.0407, -0.0626]],\n","        device='cuda:0', requires_grad=True), Parameter containing:\n"," tensor([-0.0445,  0.0594, -0.0306,  0.0389,  0.0110, -0.0149,  0.0269,  0.0607,\n","         -0.0429, -0.0518, -0.0168, -0.0207,  0.0532,  0.0218,  0.0264, -0.0624,\n","          0.0048,  0.0590,  0.0526, -0.0615, -0.0377, -0.0209,  0.0118,  0.0371,\n","         -0.0447,  0.0246, -0.0446, -0.0105,  0.0560, -0.0037, -0.0359, -0.0098,\n","          0.0143,  0.0351, -0.0067,  0.0391,  0.0126,  0.0136, -0.0524,  0.0425,\n","          0.0571, -0.0627, -0.0369, -0.0518, -0.0213, -0.0524,  0.0201,  0.0171,\n","          0.0535,  0.0013], device='cuda:0', requires_grad=True), Parameter containing:\n"," tensor([[ 0.0737, -0.1118, -0.0680, -0.0045, -0.0008, -0.0795,  0.0562,  0.0638,\n","           0.0929,  0.0677,  0.0055,  0.1197, -0.0224, -0.0008,  0.0043,  0.1185,\n","          -0.0245,  0.1181,  0.0961,  0.0801, -0.0647, -0.0281, -0.0539, -0.0958,\n","           0.1136,  0.0325, -0.0341,  0.0966, -0.1018,  0.0317, -0.1124, -0.0018,\n","          -0.0340, -0.0376,  0.0431, -0.0545,  0.0930,  0.1362,  0.0215,  0.0852,\n","           0.0448, -0.1384,  0.0831, -0.1367, -0.0191, -0.0243,  0.1061, -0.0876,\n","           0.0215, -0.0078],\n","         [-0.0673, -0.0171, -0.0577, -0.0983, -0.0144,  0.0085, -0.0459,  0.0094,\n","           0.0489, -0.0817, -0.0081,  0.1207, -0.1364, -0.0449, -0.0434, -0.1113,\n","           0.1037,  0.0724, -0.0737, -0.1397, -0.0476, -0.1309, -0.0312, -0.1073,\n","          -0.0312, -0.1076,  0.1229,  0.1061, -0.0671, -0.1161, -0.1393, -0.1387,\n","           0.0074, -0.0188, -0.0546, -0.1219,  0.0953,  0.0538,  0.0908,  0.0900,\n","          -0.1019,  0.1343, -0.0877,  0.0545, -0.0023,  0.0218, -0.0816,  0.0385,\n","           0.1408,  0.0461],\n","         [-0.1328,  0.0509,  0.0772,  0.1313,  0.0670,  0.1019,  0.1091, -0.0757,\n","          -0.0502,  0.0846,  0.1208,  0.1104,  0.0135, -0.0450, -0.0217,  0.1193,\n","           0.0423, -0.0308, -0.0764, -0.0585, -0.0242, -0.1381,  0.1191,  0.0467,\n","          -0.1169, -0.1240,  0.0362, -0.0038, -0.0984, -0.0627, -0.0149, -0.0095,\n","          -0.0667,  0.0940, -0.1136, -0.0673, -0.0385, -0.1232,  0.0633, -0.0243,\n","          -0.0488, -0.0194,  0.1343, -0.0602, -0.0412,  0.0565,  0.0375, -0.0329,\n","          -0.0336, -0.0413],\n","         [-0.0559, -0.0116, -0.1266, -0.1317,  0.0743,  0.0205,  0.0020, -0.0175,\n","          -0.1260, -0.0771, -0.1329,  0.0210, -0.1013,  0.0871, -0.0043, -0.0650,\n","          -0.0231,  0.0453,  0.1094,  0.0020,  0.0696, -0.0316,  0.0005,  0.0131,\n","           0.0283, -0.0317, -0.0273,  0.0513, -0.0827, -0.1005,  0.0562, -0.1226,\n","          -0.0165,  0.1160, -0.0164,  0.0491, -0.0038,  0.0913, -0.1346,  0.0995,\n","           0.0605, -0.0462,  0.0300, -0.0558,  0.1025, -0.1106,  0.1146, -0.1180,\n","          -0.0790,  0.0497],\n","         [-0.0784, -0.0366, -0.0817, -0.0538, -0.0137, -0.1187,  0.1249, -0.0055,\n","          -0.0371, -0.0477, -0.0245, -0.0056,  0.1096, -0.0923,  0.0272, -0.0368,\n","          -0.1183, -0.0966, -0.0533, -0.0082, -0.0487,  0.0910, -0.0782,  0.0528,\n","           0.0921, -0.0044,  0.0171, -0.1397,  0.1293, -0.1377,  0.1297, -0.0971,\n","          -0.1147, -0.0424,  0.0162, -0.1015, -0.0500, -0.0614, -0.0594, -0.0969,\n","          -0.0487,  0.0434,  0.1027, -0.0035, -0.0167, -0.0182,  0.0864,  0.0036,\n","           0.1073, -0.0679],\n","         [ 0.1255,  0.0758,  0.0897,  0.1074,  0.0955, -0.1020, -0.0010,  0.1368,\n","          -0.1382,  0.0999, -0.1338,  0.1315, -0.0309, -0.1366, -0.0738, -0.0809,\n","           0.0475, -0.1180, -0.1374, -0.0597,  0.0319,  0.0372, -0.0095, -0.1254,\n","          -0.0850,  0.1274, -0.1085, -0.1238, -0.0683, -0.0027,  0.0231,  0.1295,\n","          -0.1212, -0.1198, -0.1379,  0.0376, -0.0873, -0.1164,  0.0321, -0.0332,\n","          -0.1291, -0.0617, -0.0995,  0.0821, -0.1102,  0.0165,  0.0631,  0.1191,\n","          -0.0231,  0.1403],\n","         [ 0.0406,  0.1167, -0.1084,  0.0245, -0.0778, -0.1022,  0.0755, -0.0169,\n","          -0.0003,  0.0894, -0.1023, -0.0530, -0.1065, -0.0763,  0.0863, -0.0988,\n","           0.0432,  0.0681,  0.0500,  0.0836,  0.0049, -0.0932,  0.0479, -0.0835,\n","           0.0164, -0.0145, -0.0060,  0.1034,  0.0046, -0.0678, -0.0740, -0.1031,\n","          -0.1016, -0.0312,  0.1016,  0.1268, -0.1185,  0.0382, -0.0721,  0.0735,\n","           0.1071, -0.0704,  0.1153, -0.0823, -0.0800, -0.0456,  0.0463,  0.0692,\n","          -0.1038, -0.0877],\n","         [ 0.0245,  0.0901,  0.0110,  0.0643, -0.1210,  0.0604,  0.0921,  0.0876,\n","           0.0603, -0.0178,  0.1101,  0.0906, -0.0849,  0.0949,  0.0573,  0.1107,\n","           0.0839, -0.0016, -0.0166, -0.1153,  0.0926,  0.1373,  0.1098, -0.1312,\n","          -0.1197, -0.1197, -0.0686,  0.0404, -0.0225, -0.1093,  0.0519, -0.0625,\n","           0.1261,  0.1003,  0.0842, -0.0370,  0.1311, -0.0642, -0.0487,  0.0447,\n","          -0.0769,  0.0649,  0.0124,  0.0480,  0.0184, -0.1170,  0.0006, -0.0844,\n","          -0.1191,  0.0500],\n","         [ 0.1013,  0.0488,  0.0755, -0.1192, -0.0898, -0.1116, -0.1378, -0.1332,\n","          -0.0946,  0.1397, -0.0505, -0.0256, -0.1055, -0.1078,  0.0308, -0.0179,\n","          -0.0432, -0.0369,  0.1116, -0.1249,  0.1185,  0.0387, -0.1069, -0.0394,\n","          -0.1259,  0.0295, -0.0873,  0.1143,  0.0883,  0.0803, -0.1168,  0.0732,\n","           0.1059, -0.1376, -0.1256,  0.1134,  0.0411,  0.0430, -0.0561,  0.0222,\n","           0.0707, -0.0563, -0.0277,  0.0364, -0.0065,  0.0062, -0.0957, -0.0908,\n","           0.1349,  0.0610],\n","         [ 0.0079, -0.0243,  0.0351, -0.0532, -0.0076, -0.0170,  0.0219,  0.0984,\n","           0.0311, -0.0701,  0.1326, -0.0562, -0.0515, -0.0043,  0.1351,  0.0066,\n","           0.0544,  0.1334, -0.0486, -0.0391, -0.0495,  0.1396, -0.0232,  0.1008,\n","          -0.1140,  0.1164,  0.0342,  0.0080,  0.0443,  0.0027,  0.1006,  0.0539,\n","          -0.0768,  0.0039, -0.0064, -0.1316,  0.0426,  0.1021,  0.0263, -0.0084,\n","          -0.0327,  0.1320,  0.1295, -0.0437,  0.0434,  0.0529,  0.1125, -0.0066,\n","          -0.0104,  0.0339]], device='cuda:0', requires_grad=True), Parameter containing:\n"," tensor([ 0.0378,  0.0211,  0.0186,  0.0278,  0.0362,  0.0604,  0.1114,  0.0411,\n","         -0.0696, -0.1304], device='cuda:0', requires_grad=True)]"]},"metadata":{"tags":[]},"execution_count":72}]},{"cell_type":"code","metadata":{"id":"L9U7_JYlUZH3","colab_type":"code","colab":{}},"source":["learning_rate = 0.001\n","\n","loss = torch.nn.CrossEntropyLoss()\n","optimizer = torch.optim.Adam(mnist_net.parameters(), lr=learning_rate)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"RODQNNd4WQsj","colab_type":"code","outputId":"ad2e27e2-8a3d-4b0c-9b23-8aec1cf64e88","executionInfo":{"status":"ok","timestamp":1569499214031,"user_tz":-180,"elapsed":76383,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":697}},"source":["batch_size = 100\n","\n","train_loss_history = []\n","train_accuracy_history = []\n","\n","test_loss_history = []\n","test_accuracy_history = []\n","\n","\n","X_test = X_test.to(device)\n","y_test = y_test.to(device)\n","\n","for epoch in range(40):\n","  order = np.random.permutation(len(X_train))\n","  \n","  for start_index in range(0, len(X_train), batch_size):\n","    optimizer.zero_grad()\n","    \n","    batch_indices = order[start_index: start_index+batch_size]\n","    \n","    X_batch = X_train[batch_indices].to(device)\n","    y_batch = y_train[batch_indices].to(device)\n","    \n","    y_pred = mnist_net.forward(X_batch)\n","    \n","    loss_value = loss(y_pred, y_batch)\n","    loss_value.backward()\n","    \n","    optimizer.step()\n","    \n","  train_preds = mnist_net.forward(X_batch)\n","  train_loss_history.append(loss(train_preds, y_batch))\n","  \n","  train_accuracy_history.append((train_preds.argmax(dim=1) == y_batch).float().mean())\n","    \n","  test_preds = mnist_net.forward(X_test)\n","  test_loss_history.append(loss(test_preds, y_test))\n","  \n","  accuracy = (test_preds.argmax(dim=1) == y_test).float().mean()\n","  test_accuracy_history.append(accuracy)\n","  \n","  print('accuracy:', accuracy, ', loss:', test_loss_history[epoch])"],"execution_count":0,"outputs":[{"output_type":"stream","text":["accuracy: tensor(0.9395, device='cuda:0') , loss: tensor(0.2262, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9505, device='cuda:0') , loss: tensor(0.1745, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9513, device='cuda:0') , loss: tensor(0.1579, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9574, device='cuda:0') , loss: tensor(0.1478, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9555, device='cuda:0') , loss: tensor(0.1521, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9569, device='cuda:0') , loss: tensor(0.1510, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9580, device='cuda:0') , loss: tensor(0.1433, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9588, device='cuda:0') , loss: tensor(0.1336, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9546, device='cuda:0') , loss: tensor(0.1526, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9586, device='cuda:0') , loss: tensor(0.1393, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9474, device='cuda:0') , loss: tensor(0.1629, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9600, device='cuda:0') , loss: tensor(0.1354, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9554, device='cuda:0') , loss: tensor(0.1481, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9522, device='cuda:0') , loss: tensor(0.1592, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9565, device='cuda:0') , loss: tensor(0.1475, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9549, device='cuda:0') , loss: tensor(0.1441, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9555, device='cuda:0') , loss: tensor(0.1401, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9553, device='cuda:0') , loss: tensor(0.1493, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9590, device='cuda:0') , loss: tensor(0.1296, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9589, device='cuda:0') , loss: tensor(0.1375, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9595, device='cuda:0') , loss: tensor(0.1334, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9594, device='cuda:0') , loss: tensor(0.1290, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9586, device='cuda:0') , loss: tensor(0.1373, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9573, device='cuda:0') , loss: tensor(0.1381, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9620, device='cuda:0') , loss: tensor(0.1276, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9627, device='cuda:0') , loss: tensor(0.1323, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9620, device='cuda:0') , loss: tensor(0.1303, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9618, device='cuda:0') , loss: tensor(0.1339, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9623, device='cuda:0') , loss: tensor(0.1258, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9656, device='cuda:0') , loss: tensor(0.1185, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9639, device='cuda:0') , loss: tensor(0.1209, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9620, device='cuda:0') , loss: tensor(0.1280, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9601, device='cuda:0') , loss: tensor(0.1291, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9645, device='cuda:0') , loss: tensor(0.1232, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9622, device='cuda:0') , loss: tensor(0.1266, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9615, device='cuda:0') , loss: tensor(0.1261, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9656, device='cuda:0') , loss: tensor(0.1198, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9647, device='cuda:0') , loss: tensor(0.1192, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9610, device='cuda:0') , loss: tensor(0.1296, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9645, device='cuda:0') , loss: tensor(0.1210, device='cuda:0', grad_fn=<NllLossBackward>)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"23bmDXHrYjdB","colab_type":"code","outputId":"8176687a-547f-489f-b0dd-3878cd932976","executionInfo":{"status":"ok","timestamp":1569499679429,"user_tz":-180,"elapsed":1189,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["plt.plot(test_loss_history, label='test')\n","plt.plot(train_loss_history, label='train')\n","plt.legend()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x7f614042bb38>"]},"metadata":{"tags":[]},"execution_count":83},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAX0AAAD8CAYAAACb4nSYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTdN2CiHuFkKcY73bvUAy8JIQYqsQ4k3rY2uB3yIPHBuAu63XB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aMyOcvujMMn9ModIdh0\nuI7/bjvKr+LSiNbtlQ0HbkWIMeOwqZOYCXWHHN+mb+Lap3cSM+W4zFJVwWMkGBu5ozRNmw1cDtwv\nhBiQlNY07XFN02ZrmjY7J8d5CsE0UdGyPtuM6GuaNb2Tayf6kp8tHs/Fswp44MN9fPNvn3HhI2vY\nfKSOWxaP54vbz+B/vjnJqeADxEZbKMxK5Ip5o/jXtfOoburg/Ie/YGupB5FnU4XNtMzAOzsqOHy8\nlRsXjmHsSCnOu74u5YNdzn3r39go65Zzsxx/0RZPziPKImxVPOkj5WXdYYf333S4jrv+u5NFE3L4\n2WJr5YT+/iXZ/V+m5PVtmv/6W5M5YUQqP39pG6W1bjxVPKGhDFLz2XykjqueXMczaw6xq86i7JUH\nI/6K9O0rd4yoJq0BmBH9cmCk4fcC63Wm0DSt3Hp5EPgYmOHB+rwnweTmXWezzCsm59qGhrTYShSF\nEPzhgqmcNWkYda2d/Ppbk/ni9jP48ZnjSE900TrugJNGZ/HKD04hITaK5Y9/yXs7TXaoNlUOEH1N\n03jk4wOMzk7iGyfk9eVFx6X08D+vbaehtWvA0+yvamJfmbRHEE4GqGQmxTKvOJOVOyrkOMmYBPne\n1A8U/ab2Ln76ny3kpcVz//IZRFmsUbTRd8dIis1/Jz4mioevmElvr8biv37Cz/6zlTUHanzfCG8s\npy4ml6ufWk92ShxjcpJYfcg6sUuVbQ4uPI302xscV5FV77H6LDk4eIycI1OTus++wpTobwDGCSGK\nhRCxwHLAVBWOECJDCBFn/TkbmI9hLyCgmLVisDZmSdHPBMQAK4aYKAtPXDWbNbefwbULikmMNZMV\nc8zYYcm89sP5TMhL5cbnNvHU5yY+jA5E/7N9New82sj3F46WYhubAsLCeZOSON7SyV1v7RzwNC9t\nKiPJYj0YuPDSXzYlj4PVLeyrspZ/phc6FP27/7uL8ro2/nrJiaQlGPYp9IHoSXYpJDv/nVFZSbzy\nw1O4YGYBH+w6xuVPrOO0e1dz/wd7vYv+e7rRmip4aZ9GanwMK64/iR+cPpa9jdb/L5XXH1zo30+z\n1Tvg+MDtqHJHp8DapKVSPH24FX1N07qBm4H3gN3Ai5qm7RRC3C2EOAdACDFHCFEGXAw8JoTQFWcS\nsFEIsQ1YjczpDy7R1330k4fJ2t7ELIf+O0KIfhu5vpCdHMcL15/ENybncvdbu/i/N3cOtD/Q6e2V\nKRF9EImVRz85QG5qHOfNMHSexqUyLLqNm04fw6uby/ulebp7enl1czmLiq0RvgvRX3JCHkLAO9v1\nFM8oqD/S7z7v7qjgpU1l/PD0scwu6r/XQIsu+g4i/fYG6LQJ+vjcFP5w/lQ2/Oos/rb8RIqykvjb\nh/s49c+rufyJtby2pUwOuTHBoUMHEFovx6NyeP76k8hPT+Cc6SOwJCrTtUGJJ+kdZ125miYjfftN\nXJ1hk2SNv6rX78NUTl/TtJXjf8DyAAAgAElEQVSapo3XNG2Mpmm/t173G03T3rT+vEHTtAJN05I0\nTcvSNO0E6/VrNE2bqmnadOvlk4H7U+wwm97RRV8X1aRsl5Uq/iIhNoqHr5jFdQuKeWbNIb7/r02O\nxa21Bnq7+0X6W0vrWXPgONctGE1ctKECx+q/c/MZ45iYl9IvzfPpvmqqmzpYNtH6BXMxH3dYajyz\nCjN4Z4c1Kk8vlLlyq5fRscZ2bn91O9MK0vjJWeMGPoHuVGqsoAHb3+DAVz8+JopzT8znuevm8fkv\nz+CWxeMpq2vjZ//ZxoI/fcQDH+6jrsX5YPVDNS38bsX7AFy9bAGFWfLvi422sHCazPUeKTedlVQE\nA/0grBdeuMJZV25DmfSjcpTPBxnI5c9UdgwGwrMjF7yI9K0VJEk5QbNXjrIIfvWtydx1zgl8WHKM\nm1dstg1q19HTIak20X/04wOkxkdz2Ty7Sier3UBstIW/XDyd2pZO7vqvPOl6aWMZmUmxzMm3bjq7\nGZW4dEoeJZVNHKppgYxR8sDTeBRN07j15a9o7+rhr5ee6LjOvrlKfkmj7EpTU8zNys1PT+DHZ47j\n41+czorr5jE1P4373t/LKfd8xJ1v7BiQ+imra+WKf6wju1eeoY0oHNvv9iWz5Kn/59sPuHxdRZBp\nq5czraNMpEsT9TJsu0i/Rq/ccRLpg0zxVO5QTVpWwl/03XnRNB+TowP1zaSk7KDbK199ShF3nzuF\nD3ZXcdsrX/XfzNQF0hol769q5r1dlVx1chHJcXZfFoO98pT8NH64aCyvbinnpY2lfLD7GOeeOIKY\nXmsnrItIH6ToA9JuWW/2qj/Ms18e5tO91fzv2ZMZk5Ps+MEtVQNTO4a/wazFssUiOGVsNk9fM5dV\nPzuNs6cNZ8X6Iyy8dzU3rdjMV2X1VDa0c/kT6+Sm8hzrgSw1v9/zpGTKtRwsLaOiQVXwDBrMWDDo\nJDhJ71SbEf05corb0S2erzEMCW/R13qh082czCZruaaer0/MDskgle+cNIpbFo/n1c3l/O7t3bJy\nBmz18dYo+fFPDxAbZeG784sGPomdvfLNi8YyMS+F2175iq4ejYtnjbSVLca6Fv2CjESmFaTx7o4K\nmdMHKg/v5Q8rd7NoQg5X2p9lGGmpkXsk9uhnU1746o/PTeEvF0/ns9vO4PrTRvPpnmrOefALFt/3\nCbUtnTx77TzyOC5LdeNT+z84Lg0NQSotPPPFIY9fWxEg2ushwURqB2w5fftIv7pEfmf1xkpHFMyR\nlyrFA4S76IP7FE/zsf4ClZQjP4w9A0seA82PzhjLNfOLeOqLr3n4Y2sqQhfI5FwqGtp4bUs5l84Z\nSXZy3MAnsHOT1NM8UUJwwohUJo9ItYm+m0gfZLS/rayBci0LDcGHX64nKS6aP100zfWmdrOTSD8h\nQ3bL+mCJkJcWzx3LJrHmjjP41dmTGJ+XwtPXzOHEken9LZWNWCyI+FSmZWusWHeEpvbg/98qHGDG\nS18nLlXOWXYU6buK8kEeEDLHqAoeK97XHg52jLNRXZUBNx/r71Wjbz62Hh9QMRNohBD8+uzJ1Ld2\nce97e0hPjOGKpgopoFExPPX5Pno1uP7U0Y6fwMH0rCn5aTxx9Wxy9IOEPlzaTU4fYNmU4fz53T28\nW1LHxbE5xLWUc8/yqQxLcd6MBsj0mCPRF6Jfrb4vpMTHcN2po7nO+F40lPZ31zQSn860LGiq7OY/\nG0r7P84JJZVyuM7EvFQ39/Scnl6NraUyPVXV1E51UwdVTR39LkdlJfLU1XM8t+0YKrTXO9+AtUcI\na8rWIPqaJiP9KRe6f3zBHDjwoerKJiJE30Skr3t0gE30W6qDLvog89h/vmgajW1d/Or1HZw16hC5\nKcOpb+1kxbojfHvacOcWDgnp0ku+qx1ibMK8aILhTKYv0ncv+sXZSUzMS+Gpz79mWnsGM1MbGH2C\nm/ekqx06Gp2bs6UMD5z5WWM55M9yfFtCOpmWVuYWZ/L0F4e4+pQil2Zvb247yi9e3EZnTy+njsvm\nhtNGs2Bstl/Kdju6e/jRii2sMpTURlsEOSlx5KTEMSItnhNGpPLG1nJuWrGZp6+Z439jusGAmVGJ\nRhIy+0f6zVUyneku0gfZpPXVC9LKIbPY46WGE5Et+j3d1vyzQcgcWDEEm5goCw9dMZOrnlzP8YpD\nROePZsWXh2np7OHG0wdaK/dhHAIe40Sc+yJ9c94/S6fkcf8H+6hNHs6sqP3uH9BnweDY24eUPGl3\n62+62uTZmaP0DvSlvm44dTTXPbuRldsrOPfEgffVNI1/fPY1v1+5m7nFmSwcn8Mzaw7xnSfXM3l4\nKjecNpqzpw33WoRbOrr5/r828fn+Gm5dMoEzJw1jWEo86QkxWCz9DyhzizO57eWvuPPNnfz+vCl+\n6xMZNHiS3gGZ1zd+n+2nZblCb9Iq2xjxoh+G4YMVM6LfUg1o/XP6DqwYQkF8TBRPXD2b4ZYGPiyz\n8I/Pv+aMicNcpxr6jOZcNCF1toKwQJQ5C4kLZhQwblgy06ZMw9J01P1eh7PGLJ2U4bYyWX/S56M/\n0vHt1r6NMyYOY3ROEk98dtC2WW6lp1fjrv/u4vcrd3P2tOE8N7+Gm0aV8/kvF/HnC6fR2dPLT/+z\nldPv/ZgnP//a9KwFnfrWTq58ch1rDtTwl4unc9OisUzMSyUzKXaA4ANcMnskNy4cw4p1R3g6iBvQ\n7V097Kl0UwDhK11t8qzUl0jfTOWOzrDJEJOkmrQIZ9HXIwhXom9fow/90zshJi0WMrR6WuKyaWjr\n4saFLqJ8sH2BXM0G7mqTUb7JqLEwK5H3b1nI8FETZDVUQ6nrB+iVT46qdwBScmX6p8P9hC+P0Cdm\npbqI9NvqsVgE1y0YzY7yRr48aDuwt3f1cPOKzTyz5hDXLijm78tnELv6LvjkT8RFR3HJnJGs+ulp\nPHn1bPIzEvjtW7s4+Y8fct+qPQ59juypampn+eNr2VneyMNXzBowwMYZty2ZwDcm5/K7t3fxUUkA\nDpYGDlQ389u3djHvDx+y5P5P+ev7ewccGP2GHpiY8d3RSbTL6VeXyGqtZBMurVHRsklLjU8M4/RO\nTLwUNzOib8zdx6fLKoFBIPr6+i5YOIe8tJnMKXLzBYnXB8K7iPS7Wk3l8wfQV6t/BDJdbII2m4j0\nQf5tcU7q/L1BH57iLL2TYK1s0jQumJnP/1u1h3989jWnjMmmvrWT6/65kU1H6vjV2ZNsm7xNFf2c\nOS0WwZmTcjlzUi5bjtTx2CcHeeCj/Tz9xSG+O7+IaxcUOzThK61t5TtPrqOqqYOnvjuHBeOyB9zH\nGRaL4P7lJ3Lxo1/yoxVbeOWHp7g829M0jQ93V/HoJweIi7Ewa1Qms0dlcGJhOqnxAzeEO7t7WbWr\nkn+vPcKXB48TbREsmZJHlBD87cN9HG/p4K5zptjM9PyF/r30JL2jR/r6Zmz1HpnaMZv2KpgDax6Q\nZ7tuSpbDmfAVfbDu9rsQQKPvjo7FYh0GHvxa/QE0yg3PtGGFLBvvYjKKjpn0Tlebl6Iva/WdWSz3\n4Ta9o3flVjgc/eg1enrHVaRvtVeOj03kqpOL+OsHe1ldUsXv3t5FaV0bD142k7OnWd/n9kbpwNrZ\nIic8RfcvkZ1RmMGj35lFSWUjD3y4j79bxf/qU0Zx3YLRZCRJ8d9f1cSV/1hPa2c3/7p2HrNGeRDZ\nWkmMjebJq+dw7kOfc+0zG3n9pvnkpAws2d1wqJY/vVPCxsN1FGUlkhQXzYMfyYovIWBCbgqzizKY\nPSqT0TlJvLujkhc3llLT3ElBRgK3LpnAxbMLGJYSj6ZpjEhP4NFPDlDX0sV9l07vb/nhK30Omx7m\n9Hus07Zik2Q37vil5h8/cq7sLK/YCqNO8Wy9YUQEiL6H6R0IqhWDS/QqF7NVRKbSO60yt+kpqfkg\nogYYrw2gpUYaXDmLpDzsyjVNY5ncPI520L8AhvemHmITufKkQh7+eD/XPLOB1Phonrt2HnOLDcZx\nfevToL4UsscOeEqQ5ZwPXzGLPZVNPPDRPh7++ADPWKuDThmTzY9f2IJFCP7z/ZOZNNz70s+8tHj+\ncdUcLn5sDTf8ayPPX39S3+SzkspG7n13Dx+WVDEsJY7fnz+FS2aPJCbKQnNHN1uP1LPxcC2bDtfx\n+pajPLdW/h9ahByRecVJhZw2LqdfNC+E4PZlE8lKiuX3K3dT39bJY9+ZPbAL3Fs8MVvTsXbltjfW\nEJ/QLs/GzeTzdfKtVXql60Mi+oePt7C1tJ5zpo8I6aZ8ZIt+0zH5obMXihBYMTjEzoLBLXqk7zK9\n42WkHxUtUycOLJb74awxS8cY6fuThjLnqR0w7PHUQ+oIspLj+N6CYt7dUcnj35nFuNyU/vdvMkwK\nqzvkVPR1JuSl8NDlM9l7rIkHPtzHI58c4OGPD5CfnsBz182jONuLA60dUwvSuP/SE7nxuc3c9vJX\n3LpkAn99fy+vbS0nOS6a25ZO4JpTikmItUXkyXHRLBiX3ZdS6unV2FPZxJ5jjcwrzmJEuuvPwvWn\njSYzKZbbXvmKyx5fy9PXzHHcGOgpLrz0m9q7OHy8ldLaVkrrWjlS20ppbRujqo5yN3DJX9/iihnZ\nXAqeiX5yDmQUh6Qzt6qxncseX8vRhna2HKnnzm9PDpnwh7nop0ONizLD5mOOo+jEbPdpjGDQVCH3\nFxJN5oCjYmQU7za942U+04HF8gCc+e7oxKXK1/d3pN9QDjkuGn2Mkb6V25ZM4LYlExx/+Yzrqz9k\nehnjc1N48PKZ/ORYE29uO8rl8woZnubFQdYJS6cM59YlE7j3vT289dVRYqIs3HDqaH5w+hhTQ32i\nLILJene2SS6cVUBGUgw//PdmLn70S5793lzPxn06womX/r/XHeauN3fRaTAeTEuIYWRmAtOycqEc\nFhfFsm3LOi6NgT09wzFRsGmj8GTY+6705LIEp46ltbOba/+5kfq2Ls49cQTPrDlEZ08vvzt3isOq\nrUAT5qJvIr3jqMpk0KR3KmUPgScfTjv/nQF0tdqGnXtK+ijY/4Hr+7TUuN7oFUKm0/wp+pomG7PG\nnOH8PsZIv28pLr5wuueRJcb5bFYXjMtN4eff8EiOTPPD08fQ1N5NU3sXN58x1q8HFWecMTGXf183\nj2ue3sCFj6zh2Wvn+tap3FYPCFl9g2xY+783d/L8+lIWjs/hsrkjKchIZGRmom1AT1U2PAw/OjmT\n0sQuWvfFc/azX3Pjwmh+dOZYl3sO7V09vL/rGJVl+VzfVsstDz5PXco4UhNiSImPJjU+htSEGFLj\nYzh9Qo7bMyCz9PRq/PSFrew82sATV83mjInDGJGewCMfH6Czu5c/XTjN/5vkbogM0XfWet18zGbG\nZCQpWxq12XW2Bp2mo553BcenBSa9A7KCp7nS9fvSXAUj57l+HuuAdL/RXi83XV2ldxxE+i5pqpCC\nlJLnlegHEj3fHmxmjcrkpRtP4aqn1nHxI1/ywOUz+nd7e0J7vTTGs1g41tjOjc9tYsuRem5aNIZb\nFk9wLIQGp82RPaV0503kvKyRPLh6P6t2VXLvRdOZPtJ25qBpGl+VNfDSplLe3HqUxvZuZqaO4Xrg\nxJ7tvNQ8ioM1LTS2ddHU3k231d02JS6a3543xTagyAfueWc3q3Yd485vT+bMSXLv8LYlE4iPjuKv\nH+ylo7uX+y6ZHtSO6/AX/Z4OKXT2G4uaZnPYtKfPf6fGuZdLMGiqhCzXueQBWOvRndLV4n16J8Na\nwdNQCtkOhqf0dMuuWFfpHZBCWrHNuzU4oq8xy8X/lYNI3yVNFXKdGUWDTvRDyYS8FF794Xyu/+dG\nvvfMBm5fOpEbThvteX7a2o276XAtNz63mZaObh65YibLprrYvzI2XFbvIXr0Qv5y/nTOnjacO17Z\nzvkPf8H3F47hinmFrNxewcubyth7rJm4aAvLpuRx0ayRnDImCx74I1fllXLV8gV9T61pGm1dPZTW\ntvE/r23np//Zyuo9Vdx97pT+o0A94N/rDvPEZ19z9cmjuGa+rQtYCMFPzhpHXIyFe94pobO7h79f\nNpPY6OAIf/g2Z4HrrtyOJuhucyL6obdiAKTwpI7w7DFu0zs+RvrgfDO3rZYBHc6O0CN9fzX+NOrl\nmq5EPw0Q5iP9xgo5uCajSO7vBKpJaQiSn57Ayz84mW9OGc4f3ynhlhe3mR5p2UdbHcd7k1j++FqS\nYqN4/ab5rgUfIDpWzoKuOyTPgq32C4smDGPVLadx8ayRPPLxARb8aTV/WFlCclx03yjO+5fPYMG4\nbJlDLzoVDn3eb9aGEILE2Ggm5KXwnxtO4pbF43nrqwq++bfPWP91rZMFOeeTvdX85o2dLJqQw6+/\nNdnhfW5cOIY7vz2Z93Ye48bnnEzOCwCRK/p6E5Gj9Emf6Icwr9/ZKsU7IOkdHzZywfkmt7vGLJ2U\nPHnG0eGnVn+9S9hVescSJdMJpiN96zD6jFGyg9jMFDZPKN8ETy3rNy94KJEYG82Dl8/g54vHS7vv\nx76ksqHd1GM7uns4cvQou+oszB+bzRs3LWC8ffWU0xfOgCNfyp+zbXsmqfEx/OmiaTx37TxuXTKB\nD25ZyKs/nM/l8woHNqUVnyq/I8d2OHyJ6CgLPz5zHC/feDLRUYLlj3/Jve+V0GU/1c4JeyqbuOnf\nmxmfm8LfL59JtIvUzTXzi/n9+VP4qKSK6/65kdZOz6w9vCGCRV/3qXcQleobnaEU/b4afZPlmjqu\n0jua5n1HLkixtsQ4r+Bx15hlfB4w58HT3eE+ym4ol1VO7trx7eYNOKVvGL010geo+9r94zxhz7tw\nZA3U7PXv8wYRIQQ/OnMcj39nFvurmvn2g5+z+Yjjg2Nndy/rDh7nvlV7+NYDn9PVXEvOsFye9NQ6\nOiETag/Knx0YrS0Yl81Ni8YydpiLbu8ia1rn0GcuX2pGYQZv//hULppVwEOrD3DhI2s4WO3aPqSq\nqZ3vPbOBpLgonvquub6GK+aN4i8XT2fNgRqueXoDPb2BPasM/5w+OBF9J41ZMDjSO301+h5G+gnp\nchO6p3vg7NGeTumf463oW6IgfaTz9I473x0dY62+o70BnY5m+Ns0mPcDWHir8/s1lkPKCLk+VyS4\n2e/Q0YfRp44wiP5h57bN3qDPdm0ohREn+u95Q8A3TsiTef5nN7L8sbX84YKpXDAjnz3Hmvhifw2f\n769h3cFa2rp6sAiYVpBOQUIncUUjZYeYJ+gTtKLibP83npJWIOv1D30OJ9/k8q7JcdH8+aLpLJow\njNtf3c43H/iMscOSSY2XVT8phsvU+Gje3HaU2pZOXrrxZI+qqi6aVUBctIWm9u6AV/NErug3uRD9\nuBT5oQqp6OuRvoc5fb1Bq6PR9gXR0W2VY31oFEovdB7pm07vmOzK3bdKbgx/9heYvlwecBzhrjFL\nx2ykbxxR2ZfSOuT+cZ6gO0TWuzGwGyJMyEvhjZvmc9OKzfzipW387u1d1FuN6MbkJHHJ7ALmj83m\npDFZpMZFw28bPDNb09EreLLHuz/Iu6L4VNj5BvT2mHqeZVOHM6Mwgwc+2kdFfRtN7d0cqmmlqV1W\n/jRZHVdjogQPXzGLKfkmx0Aa+PZ0D7/rXhK5ot98TNoLO/rgCSEreFpDaK/sbaRvdBcdIPrmB6g4\nJX0UlLzt+LaWKvmexrv5wJvtyt31hvySd7XCB/8HFz3p+H4NZdJXxR0J6VBV4v5+fe/9CGkKl5Tj\nX9Hv6YLj1nGY7prdhhAZSbH883tzefCj/RypbeWUMVnMH5s9sOa9o1meSXliwaCjf6bNeOi7ouhU\n2PwsVG43faaVlxbPH86f6vC2nl6N5o5uLEJOdRvMhLfoxyRKEXIm+saB6PaE2oqhqQKiE9wLqD2u\n/Hc8mI/rlPRCmf7obBl4xtBSIwXSXfleXIr053EV6Xe2ykh/+mVyj+XTP8PcG6DQrgegt1dG5s6M\n1oyYjfSb+g+j93vZZu3X0Gu1Y3ZnVT3EiImy8LPFbkYgemO2ppPgL9E35PX9kF6LsgivSzuDTXhv\n5PbN1XQm+i5yz0k5oRf9lDzP53m68t/xYD6uU/Q8qqMI1Z3vjpGUPNeR/oEP5XonnwPzfyJTQu/d\n0a/MDpBnF71d5voprINU3G4MN1YAwpb6Sx/lX9HXJz4lDQurSN803pit6fgr0k8dIYelf+16Mzcc\nCW/RBxeiX+W62iMxO7TTs5oqPa/RB9dNSH5J7xh89e1x57tjJDnPtq/iCD21M2qBTLGceacsc9z+\nUv/7mWnM0jHYK7ukqUIGBPpGeEaRTCH1+KmcTt/EHXNG2EX6pujz3fEip59RJN1e86b5vo7iU2X5\np7/+X4cIkSv6TZWuRV9P74SqKUeP9D3FVXqns0Ve+pTecVGr31LjvnJHx1Wk39UuSxonfcsmvNMu\nhREzZG5f/zvA/cQsI2atGJoq+pfKZhSB1mN7LV+p3gNphTBsovxs+qtfYajgS3pn3DfgJ9v8M+e2\n6FRZ8FDpx+7wIUCEiL7dl7ynS27SuhT9HNmxaxSYYKFptuYgT3GZ3vFDpJ88DKLjB5Ztapo8SHqU\n3nHSlXtwtSw7nXyu7TqLBZbeI/PtXzxgu75vYpbJSB/cl23av/d9ZZuH3L+GGar3SEdQfZ5vmFTw\nmMaX9I4Qzqu4PKUvr/+5f55viBAhom8X6esD0VPcRPoQmgla7Q0yn+1NpB+TKBuoHKZ3Wm338RYh\nrGWbdqLf3iBTJ6ZFf7g8qDo6I9n1pjx4FZ3W//rCk+CEC+CLv9nEvqFc/j1mUgVmI/3Go9KCQcef\not/bCzX7pA+8niqLtBSPE1vloJOSJ0s/IyyvH5mi76oxSyeUVgyeDk8xIoRz/x1/RPrguFZf3/T2\nJL0DAyt4ujthz9sw4WzptWLP4rtkg9kH/yd/byyzTvUyseFtJtLv7pAeQsb+iNQRXlssD6DhiDzY\nZRsj/QjbzG2vl3n5OB+smf1F0QJrXt/9cPtwIfxFPz5d+rx0d9iu05uIkl1E0nqkH4oKHm8tGHSc\n+e/0ib6PU5zSRw3M6ZttzNJxVqv/9afygGVM7fR77UI45UdyQ7d0vfnGLDAX6TsaUal3IvtD9PWm\nrJyJMuiIio3ASL9efkZDODKwj6JTpS23P11fBznhL/oJDqK7Jhe+Ozr6tKqQRvpepHfAuf+OP0o2\nQQpve33/swn94OhJegcG+u/sfkM6KY5Z5PyxC34mD9jv3mEVfZP212Yifesw+n7pHfBfrb5erpkz\nXu5TpBVEZqQf6tSOTtGp8vLrT0O7jiASAaLvoCu3L9J3Vafvx0i/txc++bPNKMod9s1BnuIuvRPt\n42AY3VffKFaepnf01Jox0u/pht1vwYSlzgecgyzhPOtOKN8oDxquLJWNmJkh7OwsS7dY9pXqvfJv\n1z+XaSMH30ZuzX54/Yf9z479SVudd5u4gSA5R551RdBmboSKfqW83pWwxCbJNIg/Iv2avbD697Dx\naXP3b6qUU5u89chxmt5plV2+vs4GdVSr31wFwmJ+FGNcsszpGnP6hz+X+XRnqR0j05bDcGsnpdn0\njiVKvjeuIn1Xot9W63pWgRmqS/o3FqWPHHzpnc/+H2z9t1PrYZ9pq/euRj9QFJ0KR9ZGTF4/QkXf\nycQse5Ky/FO9U76p/6U7mioGphc8wWl6x4cBKkbSi+SlMfJtqZaC74kJln2t/q43ZCXOmDPdP9Zi\ngWV/lvf3pFHHnRVDU4U027MXJaPbprdomgwADD7wpBXKz2OXOS/6gNNWBztflT/X+tlOWmcwpXdA\nbuZ2tUD55lCvJCiYEn0hxFIhxB4hxH4hxO0Obj9NCLFZCNEthLjI7rarhRD7rP+u9tfCTeNI9J2N\nSbTHX1YMutgf3WKu+6+p0vvUDtjSO/Y18F2tvpVr6iRmyrMg+/SO2Xy+jl6rD9LtcPdbsvnGfrSl\nMwrnwR1lnnmnuLNX1idm2W8y+qNss6lCNgP1i/T1sk0/NX75ylcvQbf1AOTvGQI61lGJgwY9r+/G\nXz9ccCv6Qogo4CFgGTAZuEwIYT//6wjwXWCF3WMzgTuBecBc4E4hRHDP63yK9P0o+iJKim71bvf3\nb6zwvnIHZApD65FVCUZ8GaBiRAiZ1zfW6nviu6OTbIj0j6yVNg5mUjtGPLXXdRvpVzq2s/aHxXJf\n5Y5degdkKWeo0TTY9IzsfE7OhdpD/n+NjmZrSawPn29/k5QFw05Qom9gLrBf07SDmqZ1Ai8A/b6Z\nmqYd0jTtK8B+ntgS4H1N02o1TasD3geW+mHd5olLlblmXfQ1zeq7Y2LD0R/+O13tMjc6+Rz5e9lG\n1/fvm9rkQ6TvrErFX+kdGFir31JtfhNXJ8Xqv6NpMrUTHS8j/UDiLtJvOur4vU9Il++rX0R/ou26\nwdSVW7YRqnbCrO/KISOBiPT192DYRNf3CzZFC+DIOtknEuaYEf18wPiJLLNeZwZTjxVC3CCE2CiE\n2Fhd7ee6eIulf3TX0SibY8yIqj/8dyq3S+/wKRfKnHe5G9FvPS7v7+nwFCPO/Hf8ld4BW62+/t54\nld4ZDj0d0FoLu9+EsWfJDd5A4irS1zRresfJe59R5HxqmBmqS+TrG9+n1BEyKBkMm7mbnpGW11Mu\nlN42gcjp62e6OZP8/9y+UHyq1AWz+25DmEGxkatp2uOaps3WNG12To6HwmEGY1duX7mmyfROb5dv\nFRu6yOfPluP2ytx8qBw1B3mKs9JEf0f6nU3yfe1slakkb3L6ACX/lX+3p6kdb9AjfUcH8vYG1wGB\nr7X6NXtllG/cL4iKkR3FoY702xtgxysw9SI57yCjWJ71uHMk9ZSq3XKj3B+Gaf5k1HxARESKx4zo\nlwNGh6MC63Vm8OWx/sMo+n2NWWZEX/ff8SHFU75JRu2pw6XoV5e4dlX0xYJBx1V6x5dRiUb6avUP\ne16jr6P/jesek52p48Fvo/sAABiuSURBVJf4Z22uiE+XZxeOxMxdJ3RGkUxp9fZ499r25Zo6aSND\n36D11YvygDfru/J3XZT90ZtgpLrE91GHgSAxE3KnKNG3sgEYJ4QoFkLEAsuBN00+/3vAN4QQGdYN\n3G9Yrwsu/SJ9E747Ov5o0CrfBPkz5c/5swFNVvE4w9fGLHCT3vFjpA9SrDztxtXR/8aqXTB6kedT\nwrzBlRWDGdHv6XQ/5tERLTUyeHAk+qGu1dc3cIdPl5u4ICN98H9ev6pk8OXzdYoWSGuPQDWlDRLc\nir6mad3AzUix3g28qGnaTiHE3UKIcwCEEHOEEGXAxcBjQoid1sfWAr9FHjg2AHdbrwsuDtM7JqJS\nX03XWmtlF27BbPm7Lv6uNnN9tWCAIKV3DL76nvru6Bj/xmCkdsC1FYMzCwadDB8qeBxV7uikjZTO\nnqEa5lG+WRYb6FE+GCL9Q/57nfZGaZCXM0hFv/hUWa7qrthiiGNqRq6maSuBlXbX/cbw8wZk6sbR\nY58CnvJhjb7TT/QrnQ9EtyfRx0j/qLXZI3+W9fky5Yg2V5tFTRVSPKN8mLcZlwYIB+kdP27kJqTL\n16k/YjvIeJreibHOAO5sgQnL/LMud/ga6YMUQt2L3Sz6tKxsR5F+oSyxbTpqO4MKJpueln0XUwwt\nNolZ0gPJn5u5fZU7g2wTV2fUKYCAAx9B0fxQryZgDIqN3ICTkCFTHb09tjGJZhz+knw0XSvfDAib\nXQDIqL9so/OKIF8bs8BasZQ6ML3T6cf0DkCG1Ve/xRrp6wdJT8gcIztw9dmngcZVpN9UIW939h6l\njZSVNt7kuav3yMoYR+Zw6SG0WG5vtG7gXig/MzpCQGaRf9M7fZU7gzTST8iAcYth45PyfQlTIkf0\nQYqguzGJRqLjZDTrrRVD2UZ5Om/8MuXPlmcbjU72sxuP+qdxxd5/p6dLViL5K9IHmeKpPwLN1fJ9\nivHCyO2yF+CCx/23Jne4ivRdlWuCPPtKK/AyvVMC2eMcBxtp+v5ICPL621+SZ4DG1I5Ohp/LNqtK\nZC+GfsY0GFl4u8wKrH8s1CsJGJEl+m117gei25OU5V16R9Osm7iz+l9fYP3dWd7Q2zGJ9sTbOW36\na4CKEV30W6qkW6E3pOQG14fFXaTv7izL27LN6r3OI1w9+g/2Zq6mydRO3lQYMXPg7ZnF8kzO22ol\ne6p3D87KHSMFs2DcEljzoO/meoOUCBT9StdjEu3x1oqh/og8Q8i3+zLlTpV7Co6atHq65Gv5K9I3\nCltARL9QRolVuz3fxA0VruyVmyrcN8Wlj/Jc9NsbZL4+e7zj22PiZSAS7PTO0S2yeXDWdx2fgejV\nSo1H/fN6VSWDN59v5PTb5edjXXhG+5El+s1V7gei25OU450Vg75Zmz+7//XRsdIV0pGjX3MVcnav\njzl9sJquGUXfD/Nx7dGrWar3DB3Rt0TJVJR9pN/bI8t5zUT6LVVy89ksNfvkpatcdloIyjY3PSM/\nD1Mvdny7P8s22+rlgW+w5vON5M+E8cvgywddW3YMUSJL9Gv2yktPqkwSvUzvlG+SnYe5Jwy8rWC2\nY8dNX8ckGnGa3vFnTl+vNNE8r9wJJQkO5g00V8nZu+4srb2xWO6bluWgckcnPcgNWh1NsP1lmHKB\n8/4IvWzTH3n9wV65Y8+iO+T3Z92joV6J34ks0de/fK5m49qTlGP1w7H3knND+WbZ7OKo9DJ/tmPH\nTX9YMOg4Te8EQvQZOpE+WEt47UTf7AFXj3498eCpLpEBgKsNzLSR0l7Z08+Zt2x/WXrIz7rG+X1S\nC8AS7Z9Iv2qXvBwKkT7I7+7Eb8GXD4ddtB8Zoq9HMn2i72F6R+txbcdrT083VGwduImr42wzV2/M\nclVBYpaEdNlWr3cX+ms+rpG4FEiwlloOJdF3ZLpmWvSL5KUnef3qvbJyx9UGZnqhzJ/r5a+BZtMz\n0nbA2WcUICparssvkX6JDDj0pr6hwOm3Q0cDrH041CvxK5Eh+lHRMo+rn2J6tJHrRYNW9W4pss6+\nUBnFjh03myqk77439e726FUqeoonEJE+2PL6Qyq948BeWd+sdCf6iZmyackj0S9xvomrkx7Ess3y\nTTIomXm1+34Vf1ksV+2W6S1fR3UGk7ypMOnbsPaR/vM4hjhD6H/ARxLSbdGuJ1GpN6Kvb+IWOBF9\nIRw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+PYdfcT7wx5ri1M3ZeuNsvXG23szXbK+KiBe8GFJxTX+uJB2MiPW5c8zE2XrjbL1x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\n6ZuZNYibvplZg/wHDkVoByxVpfUAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"oMlrhCTYZiHd","colab_type":"code","outputId":"25256ee9-d9e6-4ed1-8602-10dd71c6bb2d","executionInfo":{"status":"ok","timestamp":1569499674275,"user_tz":-180,"elapsed":1854,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["plt.plot(test_accuracy_history, label='test')\n","plt.plot(train_accuracy_history, label='train')\n","plt.legend()\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 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DJWFgMWin5qNpws6n1f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IJWUwDJsWumOKMsyI/lFglNPtkY77upFSlqMifYQQacBVUsoaIcSZwDlCiB8CaYBNCNEgpeyz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\nc85liDd955zLEG/6zjmXIf8AblcIWszyR3YAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"uOEowxnMddbu","colab_type":"code","outputId":"af82f7ed-22aa-4a04-9511-df500537f628","executionInfo":{"status":"ok","timestamp":1569500068678,"user_tz":-180,"elapsed":365208,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["batch_size = 100\n","\n","train_loss_history = []\n","train_accuracy_history = []\n","\n","test_loss_history = []\n","test_accuracy_history = []\n","\n","\n","X_test = X_test.to(device)\n","y_test = y_test.to(device)\n","\n","for epoch in range(200):\n","  order = np.random.permutation(len(X_train))\n","  \n","  for start_index in range(0, len(X_train), batch_size):\n","    optimizer.zero_grad()\n","    \n","    batch_indices = order[start_index: start_index+batch_size]\n","    \n","    X_batch = X_train[batch_indices].to(device)\n","    y_batch = y_train[batch_indices].to(device)\n","    \n","    y_pred = mnist_net.forward(X_batch)\n","    \n","    loss_value = loss(y_pred, y_batch)\n","    loss_value.backward()\n","    \n","    optimizer.step()\n","    \n","  train_preds = mnist_net.forward(X_batch)\n","  train_loss_history.append(loss(train_preds, y_batch))\n","  \n","  train_accuracy_history.append((train_preds.argmax(dim=1) == y_batch).float().mean())\n","    \n","  test_preds = mnist_net.forward(X_test)\n","  test_loss_history.append(loss(test_preds, y_test))\n","  \n","  accuracy = (test_preds.argmax(dim=1) == y_test).float().mean()\n","  test_accuracy_history.append(accuracy)\n","  \n","  print('accuracy:', accuracy, ', loss:', test_loss_history[epoch])"],"execution_count":0,"outputs":[{"output_type":"stream","text":["accuracy: tensor(0.9607, device='cuda:0') , loss: tensor(0.1352, device='cuda:0', grad_fn=<NllLossBackward>)\n","accuracy: tensor(0.9636, 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Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":303}},"source":["plt.plot(test_loss_history, label='test')\n","plt.plot(train_loss_history, label='train')\n","plt.legend()\n","print(sum(test_loss_history)/len(test_loss_history))\n","print(sum(train_loss_history)/len(train_loss_history))"],"execution_count":90,"outputs":[{"output_type":"stream","text":["tensor(0.1222, device='cuda:0', grad_fn=<DivBackward0>)\n","tensor(0.0881, device='cuda:0', grad_fn=<DivBackward0>)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYQAAAD8CAYAAAB3u9PLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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L8peUkY3GNrftPGpMC89mPYRzEh2TjoloW1JVlKRm5Qq4VyWhvazcrmyopK/HTPzxCqTZA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\nGjUA1AahRo0aNWo41AahRo0aNWoAAP4/ellNAWXKfNoAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"W8I0Yc8Sd_qO","colab_type":"code","outputId":"45d0eca8-cc9b-48f1-adf7-b75862f01b7e","executionInfo":{"status":"ok","timestamp":1569507554635,"user_tz":-180,"elapsed":1194,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":303}},"source":["plt.plot(test_accuracy_history, label='test')\n","plt.plot(train_accuracy_history, label='train')\n","plt.legend()\n","print(sum(test_accuracy_history)/len(test_accuracy_history))\n","print(sum(train_accuracy_history)/len(train_accuracy_history))"],"execution_count":91,"outputs":[{"output_type":"stream","text":["tensor(0.9640, device='cuda:0')\n","tensor(0.9715, device='cuda:0')\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAX0AAAD8CAYAAACb4nSYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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RfZDBKTRRxmjWsB0UDuGOf0mXaa8rckMd8LkWGg3ilU18NKuzj2NoigRI2obFNi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\nKeXpUsrTAbwKwDiAu1pY/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xZOXx3ryhQ8xNBVAadfRGOcfg4D+8exfyJUWN3+bB5PbDuA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\nFhYWywjW6FtYWFgsI1ijb2FhYbGM8P8B2RK/9zCK+XoAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"6AEUXTbAeOr0","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}